• Saubhagya Vardhan
• Dushyant Singh
• Dr.(Mrs.)Neha Agrawal

With the advent of cashless economy and rising participation in online based transactional activities the number of fraudulent cases all over the world is on the rise and causes tremendous losses to the individuals and financial industry. Although, there are many criminal activities occurring in the financial industry, credit card frauds are among the most prevalent and worried by customers. Hence, to counter these frauds data mining along with machine learning is one of the prominent approaches used to prevent the losses caused by these illegal acts. First, data mining techniques were employed to study the patterns and characteristics of suspicious and non-suspicious transactions based on normalized and anomalies data. In addition to this, machine learning (ML) techniques were employed to predict the suspicious and non-suspicious transactions automatically by using classifiers. Therefore, with the combination of machine learning and data mining techniques we were able to identify the genuine and non-genuine transactions by learning the patterns of the data. This paper discusses both the supervised and unsupervised based classification techniques used for credit card fraud detection.

Credit Card, Fraud Detection, Machine Learning, Data Mining

"Comparison of Credit Card Fraud Detection Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.279-282, April-2019, Available :http://www.jetir.org/papers/JETIR1904F47.pdf

"Comparison of Credit Card Fraud Detection Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp279-282, April-2019, Available at : http://www.jetir.org/papers/JETIR1904F47.pdf